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首页> 外文期刊>Energy Conversion & Management >Employing genetic programming to find the best correlation to predict temperature of solar photovoltaic panels
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Employing genetic programming to find the best correlation to predict temperature of solar photovoltaic panels

机译：采用遗传编程来找到最佳相关性与预测太阳能光伏电池板的温度

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The best function form to predict the panel's temperature (T-panel) is found for a product family of polycrystalline solar panels, with the nominal capacities of 20, 30, 40, 50, 60, 80, 120, 150, 200, 250, 300, and 320 W. For this purpose, genetic programming is used. Experimental data recorded throughout a year is employed while in addition to solar radiation, ambient temperature, and wind velocity, ambient relative humidity is also considered as one effective parameter. First, the best function form is obtained and verified for the 40 W panel, and then, the generalization capability of that is checked for other panels. Moreover, the prediction ability of the best found function form in comparison to the nominal operating cell temperature (NOCT) and nominal module operating temperature (NMOT) approaches, as the most common ways to obtain T-panel, is evaluated using the monthly and annual profiles of errors. The profiles of error in prediction of T-panel, efficiency, produced power, and generated energy for the presented, NOCT, and NMOT models are compared together, which shows the vast superiority of the best found function to NOCT and NMOT methods. As an example, for the 50 W panel, the best found function form is able to predict T-panel, efficiency, produced power, and generated energy 2.15, 3.36, 3.03, and 3.39 times more accurate than NMOT method in a year. It also has 2.82, 4.18, 4.04, and 4.01 times better prediction than the NOCT model during the same period for prediction of the aforementioned performance criteria of the 50 W panel, respectively.
机译：预测面板温度（T-Panel）的最佳功能形式是为多晶太阳能电池板的产品系列，标称容量为20,30,40,50,60,80,120,150,250，为此目的，300和320 W.使用遗传编程。在整个一年中记录的实验数据在太阳辐射，环境温度和风速之外，环境相对湿度也被认为是一种有效参数。首先，获得最佳功能形式并验证40W面板，然后检查该面板的泛化能力。此外，使用每月和年度，评估与标称操作电池温度（NOCT）和标称模块工作温度（NOMOT）和标称模块工作温度（NMOT）接近的最佳发现功能形式的预测能力是使用每月和年度获得T-Panel的最常见方法的方法错误的概况。将误差的误差误差，效率，产生的功率和产生的能量为所呈现的，NOCT和NMOT模型进行了比较，其显示了NOCT和NMOT方法的最佳发现功能的巨大优势。例如，对于50 W面板，最好的发现功能表单能够在一年内预测T形面板，效率，产生的功率和产生的能量2.15,3.36,3.03和3.39倍。在同一时期的同一时期，它还具有2.82,4.18,4.04和4.01倍的预测，分别在相同的时间内预测Nogct模型，以预测50 W小组的上述性能标准。

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